Applying Machine Learning Techniques on Air Quality Data for Real-Time Decision Support

Fairly rapid environmental changes call for continuous surveillance and decision making, areas where IT technologies can be valuable. In the aforementioned context this work describes the application of a novel classifier, namely σFLNMAP, for estimating the ozone concentration level in the atmosphere. In a series of experiments on meteorological and air pollutants data, the σ–FLNMAP classifier compares favorably with both back-propagation neural networks and the C4.5 algorithm; moreover σ–FLNMAP induces only a few rules from the data. The σ–FLNMAP classifier can be implemented as either a neural network or a decision tree. We also discuss the far reaching potential of σ–FLNMAP in IT applications due to its applicability on partially (lattice) ordered data.